SOTAVerified

Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 411–420 of 2050 papers

TitleStatusHype
Evaluating Gender Bias in Large Language Models—0
A survey of probabilistic generative frameworks for molecular simulationsCode0
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
Mitigating covariate shift in non-colocated data with learned parameter priors—0
UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration—0
Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Games—0
Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
GeMID: Generalizable Models for IoT Device IdentificationCode0
Show:102550
← PrevPage 42 of 205Next →

No leaderboard results yet.